Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. by Enock Niyonkuru (21246879)

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A. Distributions of the observed (red), 1000 Poisson (green) and 1000 negative binomial (blue) model generated expected counts of synonymous mutations per genome per day. B. Estima... by Lele Zhao (532080)

Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... by Enock Niyonkuru (21246879)

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Schematic of the approach: This schematic illustrates the entire workflow of the project. by Enock Niyonkuru (21246879)

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Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). by Enock Niyonkuru (21246879)

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Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

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Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... by Enock Niyonkuru (21246879)

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Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... by Enock Niyonkuru (21246879)

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Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

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SupplimentaryCompressed (zipped) Folder. by Shravani Sanyal (20833595)

“…The pest identity was confirmed with morphological taxonomy, and the possible habitat distribution and further spread in future climate scenarios were modelled using the MaxEnt algorithm. …”

Test omission rate and predicted area. by Shravani Sanyal (20833595)

“…The pest identity was confirmed with morphological taxonomy, and the possible habitat distribution and further spread in future climate scenarios were modelled using the MaxEnt algorithm. …”

Bioclimatic variables used for the study. by Shravani Sanyal (20833595)

“…The pest identity was confirmed with morphological taxonomy, and the possible habitat distribution and further spread in future climate scenarios were modelled using the MaxEnt algorithm. …”

Occurrence records of <i>S. incertulas</i> in the world. by Shravani Sanyal (20833595)

“…The pest identity was confirmed with morphological taxonomy, and the possible habitat distribution and further spread in future climate scenarios were modelled using the MaxEnt algorithm. …”

Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Additional computational experiments revealed that psychosocial predictors were more important to predicting early adolescent internalizing disorders than cognitive, neural or biological factors and generated models with better performance. Future work, including replication in additional datasets, will help test the generalizability of our findings and explore their application to other stages in human development and mental health conditions.…”

Merge_DN. by Ziyang Liu (242055)

“…Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential.…”

pone.0329640.t001 - by Ziyang Liu (242055)

“…Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential.…”

Raw qPCR Ct values. by Ziyang Liu (242055)

“…Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential.…”

Daily expression of core clock genes in wild-type and <i>Clk</i>^<i>Δ</i>^<i>29</i> heads under 12:12 LD conditions. by Daniel J. Brady (20622465)

Longitudinal trajectories of functional network development across the birth transition. by Lanxin Ji (20290942)

Validation AUC scores for BCG-RP. by Saul Fuster (22339894)

“…To address this challenge, we propose a novel three-part framework comprising of a convolutional network based tissue segmentation algorithm for region of interest delineation, a contrastive learning module for feature extraction, and a nested multiple instance learning classification module. …”